Each brain imaging modality reports on a different aspect of the brain (e.g. gray matter integrity, blood flow changes, electrical activity) and each has strengths and weaknesses. Combining multimodal imaging data is not easy since, among other reasons, each modality requires specialized expertise, and thus it is typical to analyze each imaging modality separately and interpret the results independently of one another. Many mental illnesses, such as schizophrenia, bipolar disorder, depression, and others, currently lack definitive biological markers and rely primarily on symptom assessments for diagnosis. One area which can benefit greatly from the combination of multimodal data is the study of schizophrenia. The brain imaging findings in schizophrenia are widespread and heterogeneous and have limited replicability. We show evidence that, in part, the lack of consistent findings is because most models do not combine imaging modalities in an integrated manner and miss important changes which are partially detected by each modality separately. We propose to develop multivariate methods based upon independent component analysis (ICA) to enable research on healthy versus diseased brain by identifying associations between different data types. The successful completion of this research will 1) provide a powerful set of tools [stand alone toolbox and database] for identifying relationships between multi-modal data, 2) provide a set of reliable brain imaging biomarkers for differentiating schizophrenia patients, bipolar patients (who share many symptoms with schizophrenia), and healthy controls, and 3) lay the groundwork for future work towards using imaging biomarkers for clinical purposes. In addition, the algorithms, model selection methods and anonymized data we provide will enable other investigators to use our tools and to compare their own methods with our own as well as to apply them to a large variety of brain disorders.